Observational studyBMC medical research methodology2024
Deep learning models for the prediction of acute postoperative pain in PACU for video-assisted thoracoscopic surgery.
Observational study in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Leveraging transformer-based artificial intelligence for enhanced anesthetic decision-making in orthopedic surgery.Frontiers in medicine · 2026Article
- Prediction Models for Acute and Chronic Postoperative Pain in Patients with Cancer: A Systematic Review and Meta-Analysis.Journal of pain research · 2026Review
- Dynamic prediction model for early postoperative pain based on generalized estimating equations: a multi-timepoint longitudinal study.American journal of translational research · 2026Article
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Authors and funding
8 authors.
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Abstract
backgroundPostoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery.
methodsThrough a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain.
resultsA total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set (n = 931) and testing set (n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain.
conclusionsDeep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs.
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